Abstract
This paper presents a new hybridization approach to improve CEVESA, a multi-zonal hydro-thermal equilibrium model for the joint dispatch of energy and secondary reserve capacity for the Iberian Electricity Market (MIBEL). Like similar fundamental models, CEVESA provides market prices that typically show an average systematic bias compared to real market prices. This is because these models do not always capture the true variable production costs of the generation units or the additional markups that generation companies may include in their pricing strategy. Based on real market outcomes, this paper proposes a new methodology built on a previous hybridization approach that estimated a constant monthly markup per thermal offering unit [1]. This new methodology is based on a functional estimation of the offering unit cost (or bidding price), using as input the initial CEVESA production costs based on the fuel and emissions commodities' prices, correcting the power plants' markup.

Abstract
In Walrasian markets, an auctioneer proposes a price to the market participants, who react by revealing the quantities they are willing to buy or sell at this price. The auctioneer then proposes new prices to improve the demand and supply match until the equilibrium is reached. This market, common for stock exchanges, has also been proposed for electricity markets like power electricity exchanges, where iterations among auctioneer and market participants take place before the interval settlement period (ISP) until supply and demand match and a stable price is reached. We propose a Walrasian design for local electricity markets where the iterations between auctioneer and market participants happen in real time, so previous imbalances are used to correct the proposed price for the next ISP. The designs are simulated to test convergence and their capability of achieving efficient dynamic prices.

Abstract
Energy storage systems, integrated in Renewable Energy Communities (REC), are enabling the development of operation strategies together with Photovoltaic (PV) systems. Additionally, Local Energy Markets (LEM) are emerging mechanisms to enable local energy trading in RECs, the integration of storage systems can increase the community energy savings and profits. In this context, a market environment was modelled as a Markov Decision Process (MDP). In this scope, an Agent Based Model (ABM) using the Q-Learning mechanism was used to implement and to simulate a LEM and its interaction with the Wholesale Market (WSM), also considering an architecture with storage systems. The developed model was tested considering real data regarding energy consumption and PV generation. The paper describes and discusses the obtained market strategy and the profits that can be obtained with this approach.

Abstract
Power systems are evolving very rapidly namely in what concerns the technologies used to generate electricity, the diversification of commercial relationships involving different agents and more specifically the empowerment of consumers. In this scope, several countries passed new legislation to induce the installation of Renewable Energy Communities, RECs, to induce new investments at a local level, to empower end consumers and to increase their self-sufficiency. However, the way Local Energy Markets, LEMs, will be integrated into Wholesale Markets, WSM, is not yet fully established. To this end, this paper proposes a design and an optimization model to increase the mentioned self-sufficiency level, to better manage the energy produced locally, also admitting the installation of battery storage units, and to profit as much as possible of them. LEM interaction with WSM, is based on an Agent Based Model architecture equipped with a Q-learning strategy. An economic assessment is also included, in order to get insights if some level of exemption, for instance associated with some components of the Access Tariffs, have to be considered in order to induce the massification of RECs.

Abstract
This paper describes the work developed to estimate the impact of the Special Regime Generation, SRG, in the generation cost in Portugal. Till the beginning of 2021 the values of the feed in tariffs paid to SRG were much larger than the market price paid to Normal Regime Generation, NRG, and this gap was often considered as a burden subsidized by consumers. In order to bring rational arguments to this discussion, several MSc Thesis were developed in recent years at the Engineering Faculty of Porto University to estimate the global generation cost in the country considering the current feed in regime and also admitting that generation paid feed in tariffs was reduced. This implied the calculation of the new market price if SRG was reduced and conversely NRG was increased. The results of the simulations developed for 2017, 2018, 2019 and 2020 indicate that the impact of SRG very much depends on the market price along the year. If the market price is reduced (for instance in good hydrological years as 2020) the elimination of SRG reduces the generation cost. Conversely, if the market price is high, the elimination of SRG tends to increase the generation cost.